Manager, Applied Science

Posting Type
Remote

Job Overview
Every year, the global justice system benefits from the insights of Relativity AI on billions of documents – and we are global experts in leveraging generative AI to improve each and every user experience, product, legal matter, and investigation at Relativity. We are focused on helping our users discover the truth more quickly, organize their data, and act on it with confidence.

Relativity has established a set of AI Principles to guide product development and underscore their commitment to developing responsible AI. These principles include building AI with purpose that delivers value for customers, empowering customers with clarity and control, ensuring fairness in AI development, championing privacy, placing the security of customers' data at the heart of everything they do, and acting with a high standard of accountability.

As a team, we believe in exploration, experimentation, and bringing your curiosity to work every day. We know that you can't innovate without experimentation — and a little failure happens on the path to invention. We use the latest and greatest to ensure we are the best. We experiment, ship, and learn every day.

About The AI Team At Relativity
Relativity is heavily investing in developing new AI products and features for our customers. Our team spans many teams and functions across Relativity Engineering, including our data engineering team, ML Ops team, applied science team, and product-focused feature teams for our Relativity One AI suite of products.

About The Applied Science Manager Role
The Polish branch of the Applied Science team has been gradually growing and will continue to in 2026. The Applied Science Manager will join to become a single-threaded owner of the AI model & evaluations aspect of our agentic assistant specializing in search and e-discovery use-cases. You will collaborate cross-functionally within the organisation and with our customers to consistently ship improvements to one of Relativity's most critical new products, and have significant influence on its roadmap and strategy.

Responsibilities
Job Description and Requirements

• Build and lead a high-performing applied science team (hire, onboard, set expectations, coach, and manage performance).
• Translate objectives into roadmaps: define milestones, resourcing, and risks; maintain a transparent, prioritized backlog (features, experiments, and tech debt).
• Grow people and culture: set ambitious goals, promote learning and experimentation, and create a psychologically safe environment.
• Partner broadly with Product, Engineering, Design, and Customer teams to move from PoC to production at pace.
• Own modeling & evaluation strategy across the stack: retrieval, re-ranking, generation, tool use, and guardrails.
• Choose the right approach for the job (simple when possible; advanced when it pays off) with clear cost/latency/reliability trade-offs.
• Stand up rigorous evals: offline (curated datasets, regression suites), online, human-in-the-loop labeling, and quality dashboards.
• Advance retrieval quality (chunking, indexing, hybrid sparse/dense, embeddings, query understanding) and generation quality (prompting, function calling, structure).
• Productionize with solid MLOps/LLMOps: versioning, CI/CD for models/prompts, observability, rollback plans.
• Champion privacy, security, and safety by design; collaborate with Legal/Compliance on AI governance.
• Consistently ship measurable improvements to search quality, answer accuracy, and user experience.
• Represent Relativity in select customer meetings and industry events; contribute to hiring via talks, posts, and networks.

Minimum Qualifications

• Advanced degree in Computer Science, or a quantitative discipline AND 5+ years in applied ML/AI, including 2+ years managing or tech-leading applied science/ML teams.
• Excellent communication in English (written and spoken), with the ability to explain complex trade-offs to diverse audiences.
• Track record shipping AI features in collaboration with product and engineering.
• Solid grounding in statistical & mathematical modeling and MLOps/LLMOps concepts.
• Excellent analytical skills.
• Intentional user of modern AI productivity-enhancing tools.
• Strong Python and software engineering practices; familiarity with modern ML/LLM tooling and cloud (any major provider).

Preferred Qualifications

• Experience with generative AI in production as part of larger systems (agents, tool use, function calling, guardrails).
• Depth in information retrieval & search (BM25, dense retrieval, hybrid, reranking).
• Experience in SaaS/Cloud with distributed teams, and operating in regulated/customer-data environments.
• Familiarity with privacy & AI regulations and enterprise governance.

Relativity is a diverse workplace with different skills and life experiences—and we love and celebrate those differences. We believe that employees are happiest when they're empowered to be their full, authentic selves, regardless how you identify.

Benefit Highlights
Comprehensive health plan

Flexible work arrangements

Two, week-long company breaks per year

Unlimited time off

Long-term incentive program

Training investment program

All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, or national origin, disability or protected veteran status, or any other legally protected basis, in accordance with applicable law.
Relativity is committed to competitive, fair, and equitable compensation practices.
This position is eligible for total compensation which includes a competitive base salary, an annual performance bonus, and long-term incentives.

The expected salary range for this role is between following values:

and PLN

The final offered salary will be based on several factors, including but not limited to the candidate's depth of experience, skill set, qualifications, and internal pay equity. Hiring at the top end of the range would not be typical, to allow for future meaningful salary growth in this position.